Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, …
Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple ""canned"" applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions.
This edition combines clear statistical methods with a proven approach that presents a problem, then a scenario. Updated applications reflect the latest developments in business and statistics today. You work with more than 350 new and updated business examples, approximately 50 new and updated cases and hands-on exercises that highlight statistics in action.
Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern s…
Statistical Techniques in Business and Economics is a best-selling textbook designed for students in management, marketing, finance, accounting, economics, and other business fields. It offers a clear, step-by-step approach to descriptive and inferential statistics, making it accessible for all learners. Included Appendix, Glossary, and Index
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